A vibrant and healthy wireless network edge is essential to the modern economy. New technologies such as the Internet of Things, self-driving vehicles, and a host of new automation technologies rely on robust, high-speed wireless technology for operation. Unfortunately, the demand for wireless connectivity has far outstripped the amount of wireless spectrum available. Using more spectrum and improving spectral efficiency are some longer-term solutions. A more immediate approach is to explore how to flatten the demand curve - reducing peak demands through aggressive time shifting and content caching. The focus of this research is to explore how the free storage space that sits unused on mobile devices can be leveraged by content providers and network operators to radically improve wireless performance. In short, the research seeks to push data during idle network times to avoid overloads during peak times. Whether it is a crowded sporting event or a crowded subway station, the intended result of the work is mobile devices that download data more quickly while operating with longer battery lifetimes. The work seeks to make wireless network performance in crowded venues remarkably better. In this work, free space on mobile devices is made securely writable by trusted content providers and network operators, effectively allowing the network operator to push content dynamically to a device. The mechanisms and trade-offs that occur in large scale wireless systems (WiFi, cellular) present numerous challenges with respect to how and when to push content to the devices such that there is a net gain to overall wireless network system health. The work proposes to develop the architecture which seeks to allow devices in tandem with the network to sense redundant content. Mobile devices and the network operator constantly monitor and detect redundancy, triggering thresholds by which redundant content is efficiently pushed appropriately in the network through D2D (device to device) sharing and targeted broadcasts. Content is pre-staged during network idle periods or high bandwidth opportunities to time shift, improving the perceived Quality of Experience of the user in both responsiveness and efficiency. The project will develop prototype apps, create a Software Development Kit for Android and iOS, and conduct robust evaluations in demanding, dense environments.

Modern societies are witnessing the prevalence of a wide assortment of distributed cyber-physical systems (CPS) built upon network infrastructure. International standards for mission-critical CPS applications, such as industrial process control systems and avionics, require their network infrastructure to provide deterministic delay performance. However, the problem of integrating CPS theoretical concepts with real-world network performance remains largely unexplored. This project addresses this open problem so that feedback control CPS in network-challenged spaces can be analyzed formally. The project result can be applied to many other CPS application domains involving real-time control and adaptation, such as vehicular control and communication systems, industrial process control, and network-on-chip systems. Broader impacts include developing publicly-available open-source software for the research community and educating a wide spectrum of audience, from high-school and undergraduate students to academic and industry researchers, by offering seminars and tutorials and organizing a workshop with strategies to maximize the participation of under-represented groups. The main goal of this project is to establish a systematic approach to the design, characterization, and refinement of network infrastructure in CPS as a breakthrough result for designing and implementing CPS with time-critical tasks. Different from existing studies relying on predefined or presumed device/system specifications, the new approach balances theoretical analyses with empirical evaluations by exploring network-calculus-based modeling of networking devices and traffic sources from measurements. This project also focuses on non-feedforward networks, in contrast to state-of-the-art methods targeting feedforward networks, and includes investigation of compositional, algebraic, and optimization-based approaches to delay performance analysis of non-feedforward networks and research on identification and mitigation of delay performance bottlenecks in networked CPS. This project will use PLC (Programmable Logic Controller)-based industrial automation systems for case studies, not only demonstrating the usage and capabilities of the systematic approach but also providing reference implementation of related algorithms.

The goal of this research is to enable a broad spectrum of programmers to successfully create apps for distributed computing systems including smart and connected communities, or for systems that require tight coordination or synchronization of time. Creating an application for, say, a smart intersection necessitates gathering information from multiple sources, e.g., cameras, traffic sensors, and passing vehicles; performing distributed computation; and then triggering some action, such as a warning. This requires synchronization and coordination amongst multiple interacting devices including systems that are Internet of Things (IoT) devices that may be connected to safety critical infrastructure. Rather than burden the programmer with understanding and dealing with this complexity, we seek a new programming language, sensor and actuator architecture, and communications networks that can take the programmer's statements of "what to do" and "when to do", and translate these into "how to do" by managing mechanisms for synchronization, power, and communication. This approach will enable more rapid development of these types of systems and can have significant economic development impact. The proposed approach has four parts: (1) creating a new programming language that embeds the notion of timing islands -- groups of devices that cooperate and are occasionally synchronized; (2) creating a network-wide runtime system that distributes and coordinates the action of code blocks -- portions of the program -- across devices; (3) extending the capabilities of communication networks to improve the ability to synchronize devices and report the quality of synchronization back to the runtime system, enabling adaptive program behavior; and (4) extending device hardware architecture to support synchronization and time-respecting operation.

The goal of this research is to enable a broad spectrum of programmers to successfully create apps for distributed computing systems including smart and connected communities, or for systems that require tight coordination or synchronization of time. Creating an application for, say, a smart intersection necessitates gathering information from multiple sources, e.g., cameras, traffic sensors, and passing vehicles; performing distributed computation; and then triggering some action, such as a warning. This requires synchronization and coordination amongst multiple interacting devices including systems that are Internet of Things (IoT) devices that may be connected to safety critical infrastructure. Rather than burden the programmer with understanding and dealing with this complexity, we seek a new programming language, sensor and actuator architecture, and communications networks that can take the programmer's statements of "what to do" and "when to do", and translate these into "how to do" by managing mechanisms for synchronization, power, and communication. This approach will enable more rapid development of these types of systems and can have significant economic development impact. The proposed approach has four parts: (1) creating a new programming language that embeds the notion of timing islands -- groups of devices that cooperate and are occasionally synchronized; (2) creating a network-wide runtime system that distributes and coordinates the action of code blocks -- portions of the program -- across devices; (3) extending the capabilities of communications networks to improve the ability to synchronize devices and report the quality of synchronization back to the runtime system, enabling adaptive program behavior; and (4) extending device hardware architecture to support synchronization and time-respecting operation.

The objective of this research is to develop new forms of tool-supported safety analyses for next-generation integrated medical systems that are based on the concept of medical application platforms (MAP). A MAP is a safety- and security- critical real-time computing platform for (a) integrating heterogeneous devices, medical IT systems, and information displays via a communication infrastructure and (b) hosting application programs ("apps") that provide medical utility via the ability to both acquire information from and update/control integrated devices, IT systems, and displays. The intellectual merit of the project lies in developing novel forms of hazard analyses (one of the primary forms of analysis used in safety critical systems) that can overcome the unique challenges posed by MAP-based systems. The project will develop tool support that will (a) integrate hazard analyses with architectural models of MAP-based systems and (b) provide significant automation of analysis steps. In consultation with engineers from the Food and Drug Administration (FDA), the project will construct mock risk management and regulatory artifacts associated with MAP apps. The impact of this work centers around helping FDA engineers understand the architectural and safety issues associated with MAPs and identifying best practices that can lead to high assurance of MAP-based medical systems. Additionally, the project will produce concrete hazard analysis examples that will provide science-based inputs into the design of a new regulatory approach and industry safety standards that support compositional regulation of heterogeneous multi-vendor MAP-based systems.

This project focuses on designing control mechanisms for a networked system with unknown structure by making use only of non-invasive observations. By non-invasive observations, it is meant that what is being measured is not the system reaction to actively injected inputs, but rather the system behavior when it is operating under standard conditions and subject to potentially unobservable forcing signals. The capability of designing controllers based only on non-invasive observations is of paramount importance for any large scale network fulfilling critical or uninterruptible functions (i.e., a power grid, a logistic system) or in situations where it is impractical or too expensive to inject known probing signals into the system (i.e., a gene network, a financial network). Other relevant applications are in medicine (i.e., repeated drug testing, computer- assisted anesthesia). Indeed, in these cases, for obvious safety and health concerns, it is not desirable to actively test the response of a patient to a different drug dosage or treatment, if comparably useful information could be inferred from non-invasive observations. Since non-invasive observations do not always provide full information about the network's configuration, the project will also consider how to define adequate control mechanisms that are robust with respect to uncertainties in the connectivity structure. These kinds of uncertainties are not typically considered in standard techniques for control design and the development of specific methodologies is required. Combined with the capability of adapting to changes in the network's configuration, these control techniques will provide a solid foundation for the realization of a self-healing system. This project will bridge together different areas, including statistics, computer science, and control theory with a single unifying framework. New courses will be created to facilitate communication among all these communities of researchers, advancing separate fields in a multidisciplinary way.

Effective integration of large amounts of renewable energy into the grid is of utmost importance for sustainable future and greener smart cities. Due to the unpredictable variations in weather, over 80% of the available renewable energy from solar and wind sources cannot be harnessed effectively. Large scale and cost-effective integration of photovoltaic energy into the smart grid is challenging due to: (a) unpredictability and intermittency of weather pattern, (b) fast morning ramp up and afternoon ramp down of solar generation that triggers instabilities in the grid, (c) unavailability of solar generation at sun down requiring the need for locational energy storage facilities, and (d) lack of technologies for efficient and intelligent on-demand sharing of solar generation with conventional power generation in the grid. Current technologies of solar integration are based on unreliable weather prediction and ineffective load sharing that make the overall grid performance unreliable and inefficient, thus necessitating the need for a broader outlook of the whole picture. This research brings a holistic vision of the future smart grid as a synergistic integration of its various components with novel computational tools for forecasting and intelligent load sharing with distributed energy storage. The study collects real-time Photovoltaic (PV) data from the plant, conducts high-end modeling, analysis and visualization on various datasets to understand, predict and mitigate the system instabilities and fluctuations triggered by PV intermittencies. This solution can be used in the planning process at the command and control centers for electric utilities. The developed approach, which is an adaptive, resilient, efficient and effective integration of renewables, will be applicable broadly in the energy sector thereby reducing carbon footprint and making the system stable under expected high penetration of renewable sources and unanticipated intermittencies. This solution fills the gap that will help our nation steer closer to the ultimate goal of a sustainable future involving a smart clean power grid. This project will pursue several outreach activities to engage with students from underrepresented groups.

Each commuter in the United States lost on average $818 in 2015 due to congestion. More than 66% of congestion happens on city streets. The situation is steadily getting worse as the number of cars on roads increases and is expected to double by 2050. Solving the mobility problem by building new roads is not feasible. Instead, we need to use emerging technologies such as intelligent transportation systems; connected vehicles and autonomous vehicles; and new services, e.g. car sharing, ride on demand, last mile delivery services, to improve transportation efficiency on city streets. To that end, we are developing Traffic Operating System (TOS) that utilizes the existing computation, communication and automotive technologies and facilitates the deployment of new ones. TOS will increase the throughput of the urban transportation network; reduce intersection accidents by preventing red-light running and rear end collisions; and make traffic behavior more predictable, reliable and efficient. Regions that invest in a TOS could see a return on their investment in reduced transportation network and infrastructure costs, and in enhanced business and economic growth. This project will advance research in several areas of Technology for and Engineering of Cyber-Physical System (CPS). We will develop new design, analysis, and verification tools for TOS, which will embody the scientific principles of CPS, rely on extensive use of heterogeneous sensors, large-scale data collection and processing, and will actively control the dynamics of a transportation network. We will field-test traffic estimation and prediction models using sensor measurement and signal timing data from the cities of Pasadena, Sierra Madre and Arcadia in Southern California. Field test of the combined vehicle-level and traffic-flow-level control, using actual connected vehicles and vehicle-to-infrastructure (V2I) communication with a signalized intersection, will be conducted in the transition to practice (TTP) component of our project. The synergistic combination of research activities will yield novel scientific, technological and practical engineering implementation results in the design, state estimation, forecasting and control of CPS that involve transportation flows on networks. The investigators in this project plan to develop, simulate and test, through targeted vehicle and roadway infrastructure field test experiments, a traffic operating system that organizes existing computation, communication and automotive technologies to: (1) minimize congestion by increasing traffic throughput; (2) enhance safety by reducing driver errors through the use of cooperative adaptive cruise control (CACC) strategies that significantly increase arterial traffic throughput while preserving safety; and (3) contain the cost of parking by minimizing the number of idle vehicles and the number of vehicles searching for parking. These goals are achieved through integration of traffic measurements with the traffic management on vehicle, road link and network levels, making effective use of a dynamic traffic model and simulation. The project will demonstrate how three levels of traffic control are interconnected and we will develop new simulation and control design techniques that receive each other's output as feedback signals.

Software-Defined Control (SDC) is a revolutionary methodology for controlling manufacturing systems that uses a global view of the entire manufacturing system, including all of the physical components (machines, robots, and parts to be processed) as well as the cyber components (logic controllers, RFID readers, and networks). As manufacturing systems become more complex and more connected, they become more susceptible to small faults that could cascade into major failures or even cyber-attacks that enter the plant, such as, through the internet. In this project, models of both the cyber and physical components will be used to predict the expected behavior of the manufacturing system. Since the components of the manufacturing system are tightly coupled in both time and space, such a temporal-physical coupling, together with high-fidelity models of the system, allows any fault or attack that changes the behavior of the system to be detected and classified. Once detected and identified, the system will compute new routes for the physical parts through the plant, thus avoiding the affected locations. These new routes will be directly downloaded to the low-level controllers that communicate with the machines and robots, and will keep production operating (albeit at a reduced level), even in the face of an otherwise catastrophic fault. These algorithms will be inspired by the successful approach of Software-Defined Networking. Anomaly detection methods will be developed that can ascertain the difference between the expected (modeled) behavior of the system and the observed behavior (from sensors). Anomalies will be detected both at short time-scales, using high-fidelity models, and longer time-scales, using machine learning and statistical-based methods. The detection and classification of anomalies, whether they be random faults or cyber-attacks, will represent a significant contribution, and enable the re-programming of the control systems (through re-routing the parts) to continue production. The manufacturing industry represents a significant fraction of the US GDP, and each manufacturing plant represents a large capital investment. The ability to keep these plants running in the face of inevitable faults and even malicious attacks can improve productivity -- keeping costs low for both manufacturers and consumers. Importantly, these same algorithms can be used to redefine the production routes (and machine programs) when a new part is introduced, or the desired production volume is changed, to maximize profitability for the manufacturing operation .

Software-Defined Control (SDC) is a revolutionary methodology for controlling manufacturing systems that uses a global view of the entire manufacturing system, including all of the physical components (machines, robots, and parts to be processed) as well as the cyber components (logic controllers, RFID readers, and networks). As manufacturing systems become more complex and more connected, they become more susceptible to small faults that could cascade into major failures or even cyber-attacks that enter the plant, such as, through the internet. In this project, models of both the cyber and physical components will be used to predict the expected behavior of the manufacturing system. Since the components of the manufacturing system are tightly coupled in both time and space, such a temporal-physical coupling, together with high-fidelity models of the system, allows any fault or attack that changes the behavior of the system to be detected and classified. Once detected and identified, the system will compute new routes for the physical parts through the plant, thus avoiding the affected locations. These new routes will be directly downloaded to the low-level controllers that communicate with the machines and robots, and will keep production operating (albeit at a reduced level), even in the face of an otherwise catastrophic fault. These algorithms will be inspired by the successful approach of Software-Defined Networking. Anomaly detection methods will be developed that can ascertain the difference between the expected (modeled) behavior of the system and the observed behavior (from sensors). Anomalies will be detected both at short time-scales, using high-fidelity models, and longer time-scales, using machine learning and statistical-based methods. The detection and classification of anomalies, whether they be random faults or cyber-attacks, will represent a significant contribution, and enable the re-programming of the control systems (through re-routing the parts) to continue production. The manufacturing industry represents a significant fraction of the US GDP, and each manufacturing plant represents a large capital investment. The ability to keep these plants running in the face of inevitable faults and even malicious attacks can improve productivity -- keeping costs low for both manufacturers and consumers. Importantly, these same algorithms can be used to redefine the production routes (and machine programs) when a new part is introduced, or the desired production volume is changed, to maximize profitability for the manufacturing operation .